Minimal probing: supporting expensive predicates for top-k queries

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RankSQL: query algebra and optimization for relational top-k queries (2005, methodology): “The implementation of μ is straightforward from Example 3 and it is a special case (because it schedules one predicate) of the algorithms (MPro [4], Upper [2]) for scheduling random object accesses in middleware top-k query evaluation.”
Spark: top-k keyword query in relational databases (2007, methodology): “Chang et al. considered more general predicates, and an optimal algorithm, MPro, was proposed based on the necessary probing principle [5].”
Evaluating top-k queries over Web-accessible databases (2002, methodology): “As discussed in Section 3, a key difference between Chang and Hwang s MPro algorithm [Chang and Hwang 2002] and Upper is that MPro assumes a .xed query-level schedule of sources to access as random probes, and does not base its source-order choices on the current query state.”
A survey of top-k query processing techniques in relational database systems (2008, methodology): “Examples are the Rank-Join algorithm [Ilyas et al. 2004a], the MPro algorithm [Chang and Hwang 2002], and the Upper and Pick algorithms [Bruno et al.”